Superparamagnetic Nanostructures for Off‐Resonance Magnetic Resonance Spectroscopic Imaging

This study proposes a new method to generate positive contrast in magnetic resonance imaging (MRI) using superparamagnetic contrast agents. Superparamagnetic nanostructures consisting of octahedron manganese ferrite nanoparticles embedded in spherical nanogels are fabricated using a bottom‐up approach. The composite nanoparticles are strongly magnetized in an external magnetic field and produce a unique NMR frequency shift in water protons, which can be demonstrated in MR spectroscopy and imaging to be different from the bulk pool. Moreover, the particles exhibit excellent colloidal stability in aqueous media and good cell biocompatibility. Hence, these particles are potentially useful as biomarkers by taking advantage of the positive contrast effects produced in MRI.     

Novel acylflavones from Sideritis syriaca ssp. syriaca

In the framework of the detailed phytochemical analysis of the aerial parts of Sideritis syriaca, two novel acylflavones were isolated together with three acetylated flavone glycosides and acylated flavone glycosides. The novel acylflavones were identified as isoscutellarein 7-trans-p-coumarate and apigenin 7-,4′-bis(trans-p-coumarate). Their structures were elucidated by means of UV, 1D and 2D NMR, LC–DAD–MS and confirmed by HR-MS spectroscopy.     

Rank normalization of impact factors will resolve Vanclay’s dilemma with TRIF

The ThomsonReuters impact factor is a viable, widely used and informative measure of journal visibility and frequency of use. It is accurate, transparent and easy to use. It is a live and evolving system, that can broaden its scope and implement new features and methods. Some of Vanclay’s suggestions, like wider use of order statistics, or our suggestion of rank normalization might be implemented by JCR in the future.     

Accurate Computation of Electric Field Enhancement Factors for Metallic Nanoparticles Using the Discrete Dipole Approximation

We model the response of nanoscale Ag prolate spheroids to an external uniform static electric field using simulations based on the discrete dipole approximation, in which the spheroid is represented as a collection of polarizable subunits. We compare the results of simulations that employ subunit polarizabilities derived from the Clausius–Mossotti relation with those of simulations that employ polarizabilities that include a local environmental correction for subunits near the spheroid’s surface [Rahmani et al. Opt Lett 27: 2118 (2002)]. The simulations that employ corrected polarizabilities give predictions in very good agreement with exact results obtained by solving Laplace’s equation. In contrast, simulations that employ uncorrected Clausius–Mossotti polarizabilities substantially underestimate the extent of the electric field “hot spot” near the spheroid’s sharp tip, and give predictions for the field enhancement factor near the tip that are 30 to 50% too small.     

Cluster Monte Carlo and numerical mean field analysis for the water liquid-liquid phase transition

Using Wolff's cluster Monte Carlo simulations and numerical minimization within a mean field approach, we study the low temperature phase diagram of water, adopting a cell model that reproduces the known properties of water in its fluid phases. Both methods allow us to study the thermodynamic behavior of water at temperatures, where other numerical approaches - both Monte Carlo and molecular dynamics - are seriously hampered by the large increase of the correlation times. The cluster algorithm also allows us to emphasize that the liquid-liquid phase transition corresponds to the percolation transition of tetrahedrally ordered water molecules.     

Promoting quality improvement research

The authors show how an internal grant program can stimulate quality improvement research by providing technical and financial support to clinicians and employees.     

Long-time asymptotics of the Navier-Stokes and vorticity equations on R(3)

We use the vorticity formulation to study the long-time behaviour of solutions to the Navier-Stokes equation on R(3). We assume that the initial vorticity is small and decays algebraically at infinity. After introducing self-similar variables, we compute the long-time asymptotics of the rescaled vorticity equation up to second order. Each term in the asymptotics is a self-similar divergence-free vector field with Gaussian decay at infinity, and the coefficients in the expansion can be determined by solving a finite system of ordinary differential equations. As a consequence of our results, we are able to characterize the set of solutions for which the velocity field satisfies ||u(.,t)||(L(2)) = o(t(-5/4)) as t-->+ infinity. In particular, we show that these solutions lie on a smooth invariant submanifold of codimension 11 in our function space.     

Criteria for global minimum of sum of squares in nonlinear regression

Demidenko [2000. Is this the least squares estimate? Biometrika 87, 437-452] has established the relationship between the curvature of nonlinear regression and the local convexity of a sum of squares: the Hessian matrix is positive definite if the sum of squares is less than the minimum squared radius of the full curvature. In this paper, we continue developing the criteria for the global minimum of the sum of squares in nonlinear regression. In particular, the concept of the local unimodality is introduced; a function is called locally unimodal on a level set if it has a unique local minimum in each component of that level set. We show that the level of the local unimodality of the sum of squares is equal to the minimum squared radius of the intrinsic curvature of the nonlinear regression function. A new class of unidirected nonlinear regression models is introduced with an interpretation in terms of differential geometry. The criteria are illustrated by several popular nonlinear regression models.     

Semiconductor Yield Analysis and Multi-Chip Package(MCP) Dic Pairing Optimization using Machine Learning

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Achieving dynamic, multi-commander, multi-mission planning and execution

The Multi-Agent Distributed Goal Satisfaction (MADGS) system facilitates distributed mission planning and execution in complex dynamic environments with a focus on distributed goal planning and satisfaction and mixed-initiative interactions with the human user. By understanding the fundamental technical challenges faced by our commanders on and off the battlefield, we can help ease the burden of decision-making. MADGS lays the foundations for retrieving, analyzing, synthesizing, and disseminating information to commanders. In this paper, we present an overview of the MADGS architecture and discuss the key components that formed our initial prototype and testbed. Eugene Santos, Jr. received the B.S. degree in mathematics and Computer science and the M.S. degree in mathematics (specializing in numerical analysis) from Youngstown State University, Youngstown, OH, in 1985 and 1986, respectively, and the Sc.M. and Ph.D. degrees in computer science from Brown University, Providence, RI, in 1988 and 1992, respectively. He is currently a Professor of Engineering at the Thayer School of Engineering, Dartmouth College, Hanover, NH, and Director of the Distributed Information and Intelligence Analysis Group (DI²AG). Previously, he was faculty at the Air Force Institute of Technology, Wright-Patterson AFB and the University of Connecticut, Storrs, CT. He has over 130 refereed technical publications and specializes in modern statistical and probabilistic methods with applications to intelligent systems, multi-agent systems, uncertain reasoning, planning and optimization, and decision science. Most recently, he has pioneered new research on user and adversarial behavioral modeling. He is an Associate Editor for the IEEE Transactions on Systems, Man, and Cybernetics: Part B and the International Journal of Image and Graphics. Scott DeLoach is currently an Associate Professor in the Department of Computing and Information Sciences at Kansas State University. His current research interests include autonomous cooperative robotics, adaptive multiagent systems, and agent-oriented software engineering. Prior to coming to Kansas State, Dr. DeLoach spent 20 years in the US Air Force, with his last assignment being as an Assistant Professor of Computer Science and Engineering at the Air Force Institute of Technology. Dr. DeLoach received his BS in Computer Engineering from Iowa State University in 1982 and his MS and PhD in Computer Engineering from the Air Force Institute of Technology in 1987 and 1996. Michael T. Cox is a senior scientist in the Intelligent Distributing Computing Department of BBN Technologies, Cambridge, MA. Previous to this position, Dr. Cox was an assistant professor in the Department of Computer Science & Engineering at Wright State University, Dayton, Ohio, where he was the director of Wright State’s Collaboration and Cognition Laboratory. He received his Ph.D. in Computer Science from the Georgia Institute of Technology, Atlanta, in 1996 and his undergraduate from the same in 1986. From 1996 to 1998, he was a postdoctoral fellow in the Computer Science Department at Carnegie Mellon University in Pittsburgh working on the PRODIGY project. His research interests include case-based reasoning, collaborative mixed-initiative planning, intelligent agents, understanding (situation assessment), introspection, and learning. More specifically, he is interested in how goals interact with and influence these broader cognitive processes. His approach to research follows both artificial intelligence and cognitive science directions.